Argonne National Laboratory
Advanced scientific research and computing facilities
Associate Systems Modeling & Controls Engineer - Strategic Transportation Education & Partnerships, Propulsion Controls & Modeling
On-site presence at Argonne National Laboratory in Lemont, Illinois is required.
About the job
- Experience with vehicle serial communication protocols, especially CAN-HS and CAN-FD.
- Experience with MATLAB/Simulink and GitHub.
- Experience with automotive data acquisition systems and data processing.
- Experience with generative artificial intelligence tools such as Claude Code, Codex, ChatGPT, or Gemini.
- Knowledge of software development practices including continuous integration and continuous delivery, automated testing, version control, and GitHub.
- Knowledge of vehicle modeling, simulation, analysis, and Model-in-the-Loop and Hardware-in-the-Loop development tools.
- Knowledge of hybrid and electric vehicle control architecture, supervisory control, energy management, and torque control.
- Knowledge of Department of Energy Advanced Vehicle Technology Competitions.
- Skill in leadership, time management, and working as a team member.
- Skill in oral and written communications, including professional presentations and technical writing.
- Skill in Microsoft Word, Excel, and PowerPoint.
- Skill in interacting with external sponsors, including industrial professionals and government officials.
- Ability to model Argonne's Core Values: Impact, Safety, Respect, Integrity, and Teamwork.
- On-site presence at Argonne National Laboratory in Lemont, Illinois is required.
- RD2 requires a Bachelor's degree and 5 or more years of experience, or a Master's degree and 3 or more years of experience.
- RD3 requires a Bachelor's degree and 8 or more years of experience, a Master's degree and 5 or more years of experience or equivalent, or a Doctorate and 4 or more years of experience.
- Support the deployment and use of model-based engineering environments, including Model-in-the-Loop, Software-in-the-Loop, and Hardware-in-the-Loop methodologies, to analyze system functionality, control logic, and performance.
- Analyze engineering system operation using vehicle, subsystem, and test-event data to assess performance, identify issues, and support technical decision-making.
- Support the development and application of software engineering and control-system verification methodologies, including requirements traceability, version control practices, automated testing, and verification and validation workflows.
- Contribute to structured frameworks for evaluating software and control-system implementation in a manner consistent with industry-relevant engineering practice.
- Manage, process, and analyze engineering data generated during competition and validation events to support system-level performance assessment and technical reporting.
- Contribute to the development of analysis tools, data structures, and reporting methods that improve consistency, usability, and scalability of technical evaluations.
- Collaborate with internal staff, original equipment manufacturer partners, software and tool vendors, and technical subject-matter experts to align modeling, controls, and analysis activities with vehicle platform constraints, program requirements, and engineering objectives.
- Support technical steering committees and working groups related to modeling, simulation, controls, software development, and vehicle testing.
- Coordinate assigned technical work with adjacent domains to promote consistency, technical rigor, and effective integration across the broader Advanced Vehicle Technology Competition program.
- Serve as a mentor and technical point of contact for student teams participating in the Propulsion Controls & Modeling swimlane.
- Develop and deliver training materials, technical guidance, and analysis-oriented instructional content related to modeling, simulation, controls development, software workflows, and engineering data analysis.
- Work with industry collaborators and technical partners to support delivery of domain-relevant tools, methods, and training resources.
About the company
Argonne National Laboratory advances scientific discovery and sustainability by providing access to large-scale research facilities and high-performance computing for government, academia, and industry partners. Researchers use the Advanced Photon Source for atomic-level materials studies and the Argonne Leadership Computing Facility for complex simulations and data analysis. The lab differentiates itself through its shared-use, multi-institution partnerships and a focus on eco-innovation, net-zero goals, and AI accelerator development. Its aim is to address real-world energy, materials, and data science challenges by combining cutting-edge infrastructure with collaborative research efforts.
Company Size
5,001-10,000
Company Stage
Grant
Total Funding
$19.7M
Headquarters
Lemont, Illinois
Founded
1946
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Simplify's Take
What believers are saying
- UChicago Argonne LLC won a renewed DOE contract in 2026 for five years.
- 2026 launch of ChemGraph, GridMind, and AI inference services deepens Argonne's relevance.
- Activated Materials Lab opened in 2026, expanding nuclear fuels research and user-facility demand.
What critics are saying
- DOE's FY2027 request cuts Argonne funding about $155.8 million, hitting programs and construction.
- 2025 buyouts showed staff pressure; more layoffs follow if Congress accepts budget cuts.
- A prolonged federal reset or shutdown threatens APS, Aurora utilization, and Argonne's operating model.
What makes Argonne National Laboratory unique
- Aurora and APS create a rare AI-plus-X-ray discovery stack for 2026 science.
- Chain Reaction Innovations embeds startups inside Argonne with facilities, mentors, and DOE funding.
- Q-NEXT and other DOE centers keep Argonne central to U.S. quantum research.
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Benefits
Health Insurance
Dental Insurance
Vision Insurance
Life Insurance
Disability Insurance
Paid Vacation
Paid Sick Leave
Paid Holidays
Remote Work Options
Flexible Work Hours
401(k) Retirement Plan
401(k) Company Match
Professional Development Budget
Wellness Program
Growth & Insights and Company News
6 month growth
↓ -2%1 year growth
↓ -2%2 year growth
↓ -2%Researchers at the US Department of Energy's Argonne National Laboratory have developed ChemGraph, an open-source framework that uses artificial intelligence to automate computational chemistry workflows. The tool provides a natural language interface, allowing researchers to describe scientific problems in plain language, which the system then maps onto computational tasks and analyses. ChemGraph was developed using resources at the Argonne Leadership Computing Facility, including the Aurora exascale supercomputer. The framework uses AI agents to handle different workflow tasks, from planning to execution and data aggregation. It calls appropriate scientific tools to reduce the risk of AI hallucination, using AI to run physics-based simulations rather than relying solely on existing knowledge. The framework supports applications in combustion efficiency, critical materials, and next-generation batteries, and complements DOE's Genesis Mission to accelerate science through AI.
The US Department of Energy's Argonne National Laboratory has launched an AI inference service using spare supercomputing capacity to support researchers across DoE labs and the Genesis Mission. The service runs on two clusters: Sophia, with 192 Nvidia A100 GPUs, and Metis, featuring 32 SambaNova SN40L AI accelerators. The platform provides secure access to various large language models, including OpenAI's GPT-OSS, Google's Gemma, and Meta's Llama, through a chatbot-like portal. Researchers are using the service to analyse experimental data in real time, including predicting plasma disruptions in fusion energy research and processing data from particle accelerators and telescopes. Argonne plans to extend the service to its Nvidia GH200-based Tara and B200-based Minerva systems, enabling scientists to experiment with AI without building their own infrastructure.
Researchers at the US Department of Energy's Argonne National Laboratory have developed GridMind, an agentic AI system designed to assist power grid operators through natural language interaction. The system functions as a reasoning co-pilot for control rooms, simplifying complex grid management tasks. GridMind employs a multi-agent architecture where specialised AI agents handle different functions, such as power scheduling and weather-based contingency planning. Large language models coordinate these agents to analyse situations, reason across different tasks and provide explainable recommendations. The system transforms technical analysis into conversational support whilst maintaining rigorous accuracy. Tests on standard power grid models demonstrated that GridMind consistently produced correct results across multiple state-of-the-art language models. The technology aims to accelerate decision-making by integrating disconnected workflows into a coherent reasoning engine.
A research team led by the US Department of Energy's Argonne National Laboratory has developed an AI adviser that optimises machine learning algorithms during autonomous experiments, accelerating discovery of advanced electronic materials. The system was applied to Polybot, Argonne's AI-guided robotic laboratory, to investigate mixed ion-electron conducting polymers for wearable electronics and energy storage. The adviser evaluates algorithm performance in real time and communicates insights to scientists who refine experimental plans. It reduced the study to just 64 experiments from over 4,300 possible combinations. During testing, the adviser suggested switching AI algorithms, leading to significant performance improvements, and identified deposition speed as a key performance driver. The research was published in Nature Chemical Engineering and included collaborators from the University of Chicago, Lawrence Berkeley National Laboratory and other institutions.
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